The Regional Simulator (RSim) simulates changes to urban pixels for land cover maps for the five-county region around Fort Benning, Georgia, USA. Urban growth rules are applied at each iteration of RSim to create new urban land cover. The subsequent RSim modeling step then operates off a new map of land cover for the five-county region. The computer code (written in Java) has been built from the spontaneous, spread-center, and edge-growth rules of the urban growth model from SLEUTH (Clarke et al. 1997, Clarke and Gaydos 1998, Candau 2002; http://www.ncgia.ucsb.edu/projects/gig/index.html).

The urban growth submodel in RSim includes both spontaneous growth of new urban areas and patch growth (growth of preexisting urban patches). RSim generates low-intensity urban areas (e.g., single-family-dwelling residential areas, schools, city parks, cemeteries, playing fields, and campus-like institutions) and high-intensity urban areas. Three sources of growth of low-intensity urban pixels are modeled: spontaneous, new spreading center, and edge growth. First, an exclusion layer is referenced to determine the pixels not suitable for urbanization. The exclusion layer includes transportation routes, open water, the Fort Benning base itself, state parks, and a large private recreational resort (Callaway Gardens). Spontaneous growth is initiated by the selection of n pixels at random, where n is a predetermined coefficient. These pixels will be urbanized if they do not fall within areas defined by the exclusion layer. New spreading center growth occurs by selecting a random number of the pixels chosen by spontaneous growth and urbanizing any two neighboring pixels. Edge growth pixels arise from a random number of nonurban pixels with at least three urbanized neighboring pixels.

Low-intensity urban pixels become high-intensity urban pixels according to different rules for two types of desired high-intensity urban pixels: (1) central business districts, commercial facilities, and highly impervious surface areas (e.g., parking lots) of institutional facilities that are created within existing areas with a concentration of low-intensity urban pixels; and (2) industrial facilities and commercial facilities (malls) that are created at the edge of the existing clumped areas of mostly low-intensity urban pixels or along four-lane roads.

For the first high-intensity category, land-cover changes occur in a manner similar to changes in low-intensity growth, as described above: a spontaneous-growth algorithm converts random low-intensity pixels to high-intensity pixels, and an edge-growth algorithm converts random low-intensity urban pixels with high-intensity urban neighbors to high-intensity pixels. The second type of conversion from low-intensity to high-intensity urban land use is road-influenced growth and is described in the next section.

The user can influence the pattern and rate of urban growth via changes to several parameters:

Dispersion (low): This parameter influences the number of randomly selected pixels for
possible low-intensity urbanization. For dispersion (low) coefficient dL, a new
DL value is computed as DL = (dL × 0.005) × sqrt(r² + c²),
where r and c are the number of rows and columns in the land-cover map,
respectively. During each time step, DL pixels are selected at random for
attempted low-intensity urbanization. For this and all other rules defining the
creation of new low-intensity urban pixels, only previously nonurban pixels lying
outside urban exclusion zones may be changed to low-intensity urban;

Dispersion (high): This parameter influences the number of randomly selected pixels for
possible high-intensity urbanization. For dispersion (high) coefficient dH, a
new DH value is computed as DH = (dH × 0.005) × sqrt(r2 + c2), where r and c
are the number of rows and columns in the land-cover map, respectively.
During each time step, DH pixels will be selected at random for attempted
high-intensity urbanization. For this and all other rules defining the creation
of new high-intensity urban pixels, only previously low-intensity urban pixels
lying outside urban exclusion zones may be changed to high-intensity urban;

Breed (spread): This parameter indicates the probability that a spontaneously created (by the above
dispersion rule) low-intensity urban pixel is chosen to become a potential new
spreading center. For each such pixel, two of its neighboring pixels are randomly
selected for new low-intensity urbanization, if possible. A patch of three or
more urban pixels is considered a spreading center and is eligible for edge
growth, as described below;

Spread (low): This parameter indicates the probability that a low-intensity urban pixel within a
spreading center will spawn a new neighboring low-intensity urban pixel during
any time step. Such growth is also termed edge growth;

Spread (high): This parameter indicates the probability that a high-intensity urban pixel within a
spreading center will spawn a new neighboring high-intensity urban pixel during
any time step.

The SLEUTH model has been applied to more than 32 urban areas around the world. The parameters for these model runs are stored at the application’s website (http://www.ncgia.ucsb.edu/projects/gig/v2/About/applications.htm). RSim was calibrated to the five-county region surrounding and including Fort Benning by running the model in a hind-cast mode and comparing projections to U.S. census data.

A.2 Modeling the effects of roads on urban growth in RSim

The road-influenced urbanization module of
RSim consists of growth in areas near existing and new roads by considering the
proximity of major roads to newly urbanized areas. The new-road scenario makes
use of the Governor’s Road Improvement Program (GRIP) data layers for new
roads in the region. Upon each iteration (time step) of RSim, some number of
nonurban pixels in a land-use–land-cover map are tested for suitability
for urbanization according to spontaneous- and patch-growth constraints. For
each pixel that is converted to urban land cover, an additional test is
performed to determine whether a primary road is within a predefined distance
from the newly urbanized pixel. This step is accomplished by searching
successive concentric rings around the urbanized pixel until either a
primary-road pixel is found or the coefficient for a road search distance is
exceeded. If a road is not encountered, the attempt is aborted.

Assuming that the search produces a candidate road, a search is performed to seek out other
potential pixels for urbanization. Beginning from the candidate road pixel, the
search algorithm attempts to move a “walker” along the road in a
randomly selected direction. If the chosen direction does not lead to another
road pixel, the algorithm continues searching around the current pixel until
another road pixel is found, aborting upon failure. Once a suitable direction
has been chosen, the walker is advanced one pixel, and the direction selection
process is repeated.

In an effort to reduce the possibility of producing a road trip that doubles back in the opposite direction, the algorithm attempts
at each step of the trip to continue moving the walker in the same direction in
which it arrived. In the event that such a direction leads to a nonroad pixel,
the algorithm’s search pattern fans out clockwise and counterclockwise
until a suitable direction has been found, aborting upon failure. Additionally,
a list of road pixels already visited on the current trip is maintained, and the
walker is not allowed to revisit these pixels.

The road-trip process continues until it must be aborted because of the lack of a suitable direction
or because the distance traveled exceeds a predefined travel limit coefficient.
The latter case is considered a successful road trip. To simulate the different
costs of traveling along smaller two-lane roads and larger four-lane roads, each
single-pixel advancement on a two-lane road contributes more toward the travel
limit, allowing for longer trips to be taken along four-lane roads, such as the
GRIP highways.

Upon the successful completion of a road trip, the algorithm tests the immediate neighbors of the final road pixel visited for
potential urbanization. If a nonurban candidate pixel for urbanization is found,
it is changed to a low-intensity urban pixel, and its immediate neighbors are
also tested to find two more urban candidates. If successful, this process will
create a new urban center that may result in spreading growth as determined by
the edge-growth constraint.

Roads also influence the conversion of low-intensity urban land cover to high-intensity urban land cover. For the
second high-intensity urban subcategory (industry and malls), the RSim code
selects new potential high-intensity-urbanized pixels with a probability defined
by a breed coefficient for each pixel. If a four-lane or wider road is
found within a given maximal radius (5 km, which determines the
road_gravity_coefficient) of the selected pixel, the pixels adjacent to the
discovered four-lane or wider road pixel are examined. If suitable, one adjacent
pixel is chosen for high-intensity urbanization. Hence, the new industry or mall
can be located on the highway, within 5 km of an already high-intensity
urbanized pixel.

A.3 Modeling changes in land cover types other than urban in RSim

Changes within land cover types other than urban in the
RSim region can affect the potential for pixels to be urbanized. Therefore, a
brief description of that change process is included here. The annual nonurban
land-cover trend was determined by using change-detection procedures that
identify changes from one land cover type to another. Changes to and from urban
classes were not considered in the results because they were being dealt with
using different growth rules. Based on the land-cover changes happening over a
period of time, the annual rate of change was calculated. These nonurban changes
were incorporated in the form of a transition matrix from which transition
growth rules were derived. Because forest management activities differ between
Fort Benning and the surrounding private lands, the transition rules were
calculated separately for Fort Benning and for the area outside Fort Benning.
Outside Fort Benning, National Land Cover Datasets (NLCD) of 1992 and 2001 were
used. The 2001 data set covers only the northern part of the RSim study region.
The data for the remaining regions is yet to be released. Hence, currently, the
changes observed in the northern portion are assumed to be representative of
changes in the whole five-county study region outside Fort Benning. Within Fort
Benning, land-cover data sets from 2001 and 2003 were used to derive the annual
transition rules for nonurban land-cover changes.

B. Modules for environmental effects in RSim

RSim was designed to focus explicitly on how changes in land cover affect and are affected by environmental conditions. As such, the following environmental interactions are an integral part of the RSim package.

B.1 Air quality module

The air-quality module (AQM) of RSim estimates how demographic and
economic growth, technology advances, activity change, and land-cover
transformations affect ground-level ozone concentrations in the
Columbus–Fort Benning area. The AQM is largely based on air-quality
computer modeling completed during the Fall Line Air Quality Study
(1999–2004; Chang et al. 2004). Unlike the Fall Line Air Quality Study
models, though, the design of the AQM removes the computational load of
traditional air-quality modeling while remaining flexible enough for the user to
test various future scenarios. The RSim AQM estimates the relative change in the
concentration of ground-level ozone in the Columbus area
caused by changes in transportation, business and industry, construction,
military operations, and other human activities. In addition, the AQM simulates
effects on vegetation.

RSim draws on the extensive, state-of-the-art, and thoroughly reviewed ozone air quality model simulations of the Fall Line Air Quality Study (FAQS). Therein, an air-quality model was created that accurately represents a historical ozone episode for the Columbus/Fort Benning area in the year 2000. In RSim, future-year changes in human activities (sources) are used together with the FAQS base case to estimate future-year changes in ozone air quality:

ozonet = ozone2000 + (∂ozone/∂source) ΔSourcet – 2000

In the above equation, sources may change relative to how they were in the year
2000 (∆Sourcet – 2000), for example,
from economic growth in the region or changes in transportation patterns, and
these can be controlled by the RSim user. The term
∂ozone/∂source is a sensitivity
coefficient that is unique to the source and quantifies how a change in the
source, ∂source, affects changes in the concentrations of ozone,
∂ozone. These sensitivity coefficients were
calculated outside of RSim and cannot be modified by the user. The description
above assumes that only one source changes during any given period. As implemented in
RSim, the AQM really accounts for multiple changes in many sources throughout
the emissions inventory, some of which may
exasperate poor air quality and some of which may mitigate poor air quality.
Selection of the Default RSim scenario creates a future in which relative
changes in emissions sources
(∆Sourcet – 2000) are estimated with
growth factors from the U.S. Environmental Protection Agency’s
Emissions Growth Analysis System (EGAS; U.S. Environmental Protection Agency 2004).

Ozone can cause foliar damage in trees, crops, and other
vegetation, as well as other effects. RSim
simulates the effects of ozone on vegetation by using the
secondary standard for ozone to simulate the relative likelihood of effects of
ozone on vegetation. This standard is meant to protect crops and vegetation, as
well as other aspects of public welfare. The secondary standard for ozone is
equivalent to the primary standard, which states that the fourth highest 8-h
ozone concentration cannot exceed 0.08 ppm (parts per million).

B.2 Water quality and nitrogen and phosphorus export modules

The water quality module predicts changes in annual nitrogen (N) and phosphorus (P) exports from
watersheds within the five-county (Harris, Muscogee, Marion, Chattahoochee, and
Talbot) RSim region surrounding Fort Benning. It is widely established that
land use and land cover are principal determinants of nutrient export from
terrestrial ecosystems to surface receiving waters (Beaulac and Reckhow 1982).
The water quality submodel predicts total (kg yr–1) and normalized
(kg ha–1 yr–1) losses of N and P from 48 watersheds within
the region over the time frame of RSim scenarios by using export coefficients
(Johnes 1996, Johnes et al. 1996, Mattikalli and Richards 1996).

Calculations of annual N and P export are performed for the 48 12-digit
hydrological units (HUC) that are included within the RSim region. The method is
based on land-cover area (ha) within each watershed and annual nutrient export
coefficients (kg element ha–1) specific to each of the eight
land cover types. The area (ha) of each land cover category is multiplied by
its respective export coefficient, and the products are summed for all land
covers to estimate the annual flux (kg element yr–1) of N or P from
each watershed. The exports (kg yr–1) are also normalized for the
size (ha) of the watershed to yield an area-normalized N or P export (kg element
ha–1 yr–1). The 48 12-digit HUCs range in size from
approximately 3200 to 12,000 ha.

RSim predictions of N and P exports (kg element yr–1) over time vary depending on the changing patterns of land cover within each watershed. Trial runs with the water quality submodel indicate that the annual fluxes of both N and P exhibit a significant positive correlation with size of the hydrological unit (r = 0.80 and r = 0.48, respectively, P ≤ 0.001). However, the size of a watershed, the types of land cover within a watershed, and the export coefficients selected for different land covers all influence the predicted N and P exports.

B.3 Species of special concern module

RSim considers effects on the two rare species in the
vicinity of Fort Benning: Red-cockaded Woodpecker (Picoides borealis) and
gopher tortoise (Gopherus polyphemus). RSim simulates changes in
Red-cockaded Woodpecker (RCW) clusters based on changes in land cover. These clusters primarily occur in mature longleaf
pine (Pinus palustris) forest, so as land changes from evergreen forest it becomes unsuitable for
RCW. In the five-county region, most of the clusters are found within Fort
Benning. In December 2005, there were 212 known active and 96 inactive RCW
clusters at Fort Benning. According to the FWS biological opinion and the
installation’s RCW management plan, Fort Benning’s goal is 361 active RCW
breeding clusters. RSim reports how this goal is affected by changes in land cover for every year of the projection.

The gopher tortoise habitat module in RSim computes the probability of suitable gopher tortoise habitat in a
region according to a logistic regression model described by Baskaran et al.
(2006a). The gopher tortoise habitat module of RSim uses land cover variables,
distance to stream and road variables, and clay variables as inputs to derive
the probability of finding a gopher tortoise. RSim gives the user the option to
further define habitat suitability based on habitat patch size, identified within RSim using
a modification of the Hoshen-Kopelman algorithm (Berry et al. 1994, Constantin
et al. 1997). The outputs from this module are:

a map of the probability of occurrence of gopher tortoise habitat;

a map of predicted burrow presence and absence;

a table of the area of predicted burrows per year.

B.4 Noise module

Noise from military installations may cause human annoyance outside of installation boundaries. Noise can also affect wildlife. RSim uses estimates of exposure to noise from aspects of military training, namely aircraft overflights, large munitions, and small arms. Noise contour maps are developed from three noise simulation models external to RSim (Operational Noise Program 2007):

NOISEMAP calculates contours resulting from aircraft
operations using such variables as power settings, aircraft model and type,
maximum sound levels and durations, and flight profiles for a given airfield;

BNOISE projects noise effects around military ranges
where 20-mm or larger weapons are fired and takes into account both the
annoyances caused by both impulsive noise and vibration caused by the
low-frequency sound of large explosions;

The Small Arms Range Noise Assessment Model (SARNAM)
projects noise effects around small-arms ranges and accounts for noise
attenuated by different combinations of berms, baffles, and range structures.

In the implementation of RSim in the region of Fort Benning, noise contour maps represent blast noise simulated by BNOISE, as well as the negligible noise from small arms, but not aircraft noise. RSim uses these contours to estimate human annoyance and to recommend compatible land uses. Residential development and other land uses associated with low-intensity urban land cover are not compatible with blast noise > 115 dBP (peak decibels).